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Laurila, H. Simulation of spring wheat responses to elevated CO2
and temperature
Introduction
Intergovernmental Panel on Climate Change
(IPCC) has estimated that the atmospheric CO2
concentration will double from current ambient
concentration (355 ppm) and the mean tempera-
ture will increase between 1.48°C and 5.8°C by
the year 2100 (IPCC/WGI 1996). The conse-
quences of potential climate change in northern
latitudes will involve changes in agro-ecosys-
tems: Mean temperature will increase during late
winter, spring and autumn. In Finland the “SIL-
MU scenario” (The Finnish Research Program
on Climate Change, SILMU 1992–1995) esti-
mates that the atmospheric CO2
concentration
will increase from current ambient 355 ppm to
523 ppm and the mean temperature will increase
with 2.4°C by the year 2050 and respectively to
733 ppm and with 4.4°C by the end of 2100
(Carter 1996, 1998). In Finland the increase of
one degree in mean temperature will expand the
growing season for 10 days and move the bor-
der of cereal cultivation 100–200 km to the north.
In Finland a longer growing season for crops
(10–33 d) is estimated: sowing will happen ca.
10–15 days earlier (Carter 1992). Earlier sow-
ing will cause changes in growing conditions
especially during vegetative phase, with poten-
tial changes in plant phenological development
(Saarikko and Carter 1996, Saarikko 1999). It
has been estimated that C3
-metabolic pathway
plants will increase yield potential between 20
and 53% when current CO2
concentration will
double to 600–700 ppm (Goudriaan et al. 1985,
Cure and Acock 1986, Goudriaan et al. 1990).
The IGBP (International Geosphere-Bio-
sphere Programme) Wheat Network validated
several crop models with the same genotype and
weather datasets (IGBP/GCTE 1993). The spring
wheat cv. Katepwa grown in Minnesota (USA)
was used in the validation. The grain yield vari-
ation was significant between all models under
ambient temperature and CO2
conditions. The
SUCROS model (Spitters et al. 1989) grain yield
estimate for cv. Katepwa was 4.4 t ha–1
and re-
spectively AFRCWHEAT2 (Porter et al. 1993)
4.6 t ha–1
and CERES-wheat 3.5 t ha–1
(Godwing
et al. 1989, Hanks and Ritchie 1991). Porter et
al. (1993) validated the AFRCWHEAT2,
CERES-wheat and SWHEAT crop models un-
der non-limiting growing conditions. The mod-
elling results with AFRCWHEAT2 model (Se-
menov et al. 1993) indicated a general increase
of 25–30 % on winter wheat yield and biomass
levels under elevated CO2
(700 ppm) and with
different nitrogen application. However, the el-
evated temperature (2–4°C) decreased the grain
yield because of accelerated phenological devel-
opment in generative phase and thus shorter
grain filling period. When the condition of both
effects, the elevated temperature and CO2
was
simulated, the grain yield remained the same as
for current ambient conditions. In Finland Lau-
rila (1995) validated the CERES-wheat for Finn-
ish growing conditions with Swedish and Ger-
man wheat cultivars. Rosenzweig and Parry
(1994) simulated with CERES-models linked
with General Circulation Models (GCM) the
world cereal trade and production for elevated
CO2
concentration and temperature during cli-
mate change by the end of year 2060. The simu-
lation results suggest that without the net effect
of increased CO2
(555 ppm), the world cereal
production will decrease by 11 to 20 per cent.
With the inclusion of elevated CO2
effect, the
world cereal production will decrease by 1 to 8
per cent.
The overall objective of the present study was
to estimate the effects of elevated CO2
, temper-
ature and earlier sowing on spring wheat (cv.
Polkka) phenology and grain yield production
by using the CERES-wheat model. The specific
objectives of the present study consisted of fol-
lowing procedures: (i) Parameterisation of the
CERES-wheat model, consisting of (i.1) calibra-
tion of the model for Finnish long-day growing
conditions under current temperature and CO2
,
(i.2) validation of the model with independent
wheat data conducted under ambient and elevat-
ed temperature and CO2
, (i.3) sensitivity analy-
sis: the sensitivity of grain yield on CO2
and tem-
perature changes both in the potential and non-
potential models and (ii) impact assessment for
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elevated CO2
, temperature and earlier sowing ef-
fects on spring wheat phenological development
and grain yield potential under potential and non-
potential growing conditions by using the cali-
brated and validated model.
Material and methods
Both the calibration and validation procedures
for the CERES-wheat model were accomplished
by using independent data sets from different
data sources according to Thornley and Johnson
(1990). During the validation procedure, the
model was used to simulate the phenological
development and grain yield responses of cv.
Polkka to different elevated CO2
and tempera-
ture conditions. Moreover, the effects of earlier
sowing dates were simulated. Both the poten-
tial (i.e. without stress factors reducing the
yield potential) and non-potential growth under
Finnish long-day growing conditions were sim-
ulated.
Experimental data
Calibration data
MTT Agrifood Research Finland official varie-
ty trial data (1985–1990) for cv. Polkka (Svalöf,
Sweden) was used in the calibration of the
CERES-wheat model for the ambient CO2
and
temperature levels and Finnish long day grow-
ing conditions (Järvi et al. 2000, Kangas et al.
2001). The mean ambient temperature is between
10–15°C during the growing season in Southern
Finland (Hakala 1998a). The Finnish Meteoro-
logical Institute provided the required weather
data (global radiation, precipitation, diurnal
maximum and minimum temperatures) for the
CERES-wheat model.
Validation data
During 1992–1994 the cv. Polkka was grown
inside open-top chambers (OTC) under elevat-
ed (700 ppm) and ambient (350 ppm) CO2
con-
centrations and under ambient and elevated
(+3°C, inside greenhouse) temperature growing
conditions. The average nitrogen fertilization
was 120 kg N ha–1
in the experiment (1992–
1994). The open-top chamber experimental de-
sign is described by Hakala (1998a). The cv.
Polkka was sown 2–3 weeks earlier in the ele-
vated OTC experiment (+3°C) in order to simu-
late future conditions with elevated temperature
(3°C), and with a growing season 10–33 days
longer than at present (Carter 1992, Hakala
1998a, b). The cv. Polkka photosynthesis and Ru-
bisco kinetics measurements with elevated CO2
and increased temperatures are published by
Hakala et al. (1999). The plant physiological
measurements were used in the validation of the
CERES-wheat model. The observed values were
compared with the corresponding estimates of
potential and non-potential models.
CERES-wheat model description
The dynamic and mechanistic CERES-wheat
(Crop Estimation through Resource and Envi-
ronment Synthesis) crop simulation model (v.
2.10) (Ritchie and Otter 1985, Godwing et al.
1989, Hanks and Ritchie 1991, Hodges 1991)
was selected for this study because the model
was well validated and tested against data from
different winter and spring wheat experiments
(IGBP/GCTE 1993). For the latest version of the
CERES-wheat model refer to DSSAT (Decision
Support System for Agrotechnology Transfer)
web-site http://www.icasanet.org or http://
agrss.sherman.hawaii.edu/dssat/dssat/info.htm.
The CERES-wheat model can be used for po-
tential (potential model) and for non-potential
simulations (non-potential model). In the poten-
tial model, the wheat plant is growing under fa-
vourable environment. In the non-potential mod-
el, subroutines controlling soil water balance and
use of nutrients simulate the effect of water and
nutrient stress limiting grain yield (Hanks and
Ritchie 1991, Hodges 1991). The phenology sub-
model (PHENOL) simulates plant physiological
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Laurila, H. Simulation of spring wheat responses to elevated CO2
and temperature
processes controlling vernalization, photoperi-
odism and phenological development (Ritchie
and Otter 1985). The CERES-wheat model con-
tains nine growth stages (Table 1). The growth
stage classification resembles Feeke’s (Large
1954) and Zadok’s (Zadoks et al. 1974) grow-
ing scales describing both vegetative and gener-
ative growth.
The genetic coefficients
In the CERES-wheat model the genetic coeffi-
cients define the phenological development and
biomass and yield potential for different spring
and winter wheat genotypes (Ritchie and Otter
1985). The phenological genetic coefficients
used in the model are PHINT (Phyllochron in-
terval or leaf appearance rate), P1V (Vernaliza-
tion coefficient), P1D (Photoperiodism coeffi-
cient) and P5 (Grain filling period).
The phyllochron interval (PHINT) defines
the appearance rate of leaves and tillers. It var-
ies with cultivar, latitude and time of planting; a
general average value is ca. 95.0 dd (Tables 2
and 3). The P1V coefficient controls sensitivity
to vernalization. The P1D coefficient controls
sensitivity to photoperiod. The photoperiodic
effect on wheat phenological development is
modelled assuming daylengths shorter than 20
hours/day can delay development in stage 1 (Ta-
ble 1). Mean photoperiod and sunshine hours in
MTT experimental sites are presented in Table
4. A threshold daylength of 18 hours has been
identified for genotypes adapted to Finnish long
day growing conditions (Kontturi 1979, Saarik-
ko and Carter 1996). Daylengths below the
threshold delay vegetative phase from sowing to
heading. The thermal time controls the pheno-
logical development in generative phase from
heading to full maturity. The mean photoperiod
(1992–1994) in the OTC experiment from sow-
ing to anthesis was between 19 and 20 h. Ac-
cording to Hakala (1989a), the photoperiod was
long enough not to affect the phenological de-
velopment in the vegetative stage.
The yield component coefficients in the mod-
el are G1, G2 and G3 (Table 2). The G1 coeffi-
cient affects the grains/ear (GPP) and grains/m2
(GPSM) yield components. The G2 coefficient
affects the 1000-seed weight (SKERWT). The
G3 coefficient (Spike number) affects the later-
al tiller production (TPSM). Table 3 demon-
strates default genetic coefficients for spring and
winter wheat genotypes grown in different con-
tinents (Ritchie and Otter 1985, Godwin et al.
1989, Hanks and Ritchie 1991, Hodges 1991).
The CERES-wheat genetic coefficients for cv.
Table 1. Growth stages and corresponding threshold temperatures in the CERES-wheat model (Godwin et
al. 1989).
Growth stage Phase Tb
(°C)
7. End of previous crop to planting in crop rotation 1.0
Vegetative phase
8. Planting to germination
9. Germination to emergence 2.0
1. Emergence to floral initiation 0.0
Generative phase
2. From floral initiation to begin of ear growth
(double ridge phase, terminal spikelet) 0.0
3. From begin of ear growth to anthesis 0.0
4. Anthesis to begin of grain fill 0.0
5. Grain filling period 1.0
6. Full maturity*1)
1.0
*1)
Full maturity of cv. Polkka occurs ca. five days after the yellow ripening stage (Järvi et al. 2000).
Tb
= threshold temperature (°C).
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Polkka governing the phenological development
and the yielding capacity were calibrated by us-
ing the MTT official variety trial data (Järvi et
al. 2000).
The effects of elevated temperature on wheat
phenological development
The phenological development of cereals is cor-
related with the cumulative thermal time (i.e.
temperature sum) during growing season. The
accumulation of daily thermal time (DTT) is the
driving variable in the CERES-wheat phenolog-
ical submodel (Ritchie and Otter 1985). Model
calculates the cumulating DTT units from the
base threshold temperature (Tb
) (Table 1). In Fin-
land, several previous studies (Kontturi 1979,
Kleemola 1991, 1997, Saarikko 1999) have es-
timated the optimum threshold temperature for
different cereals in northern long day growing
conditions. According to Kontturi (1979) the
spring wheat threshold temperature (Tb
) in Fin-
land should be lower in vegetative (Tb
= +4.0°C)
phase versus generative phase (Tb
= +8.0°C).
Based on +5°C threshold temperature (general-
ly used in Finland), the thermal time requirement
from sowing to yellow ripening stage for spring
wheat should be 1050° ± 30° degree-days (dd).
The effects of elevated CO2
on wheat
photosynthesis
In the CERES-wheat crop model (v. 1.9), the
effect of elevated CO2
response on wheat photo-
synthesis is considered by simulating the per-
formance of the stomata. The atmospheric CO2
concentration modifies the leaf stomatal con-
ductance, which in turn modifies the rate of plant
transpiration. The stomata release concurrently
the water vapour into atmosphere as the CO2
molecules diffuse into stomatal cavity (Ritchie
and Otter 1985).
Table 2. The genetic coefficients in the CERES-wheat model (Godwin et al. 1989).
Submodel Genetic Description, process or yield component Range Unit
coefficients affected
Phenological development PHINT Phyllochron interval, leaf appearance rate <100 dd
P1V Vernalization 0–9 –
P1D Photoperiodism 1–5 –
P5 Grain filling duration 1–5 –
Yield component G1 Grains/ear (GPP), Grains/m2
(GPSM) 1–5 –
G2 1000-seed weight 1–5 –
G3 Spike number, affects lateral tiller
production (TPSM) 1–5 –
Table 3. Default genetic coefficients for spring (Sw) and winter wheat (Ww) genotypes (Godwin et al. 1989).
Genotype & Location PHINT P1V P1D P5 G1 G2 G3
Sw/Northern Europe 95.0 0.5 3.5 2.5 4.0 3.0 2.0
Sw/North America 95.0 0.5 3.0 2.5 3.5 3.5 2.0
Ww/ America/N. Plains 95.0 6.0 2.5 2.0 4.0 2.0 1.5
Ww/ West Europe 95.0 6.0 3.5 4.0 4.0 3.0 2.0
Ww/ East Europe 95.0 6.0 3.0 5.0 4.5 3.0 2.0
PHINT = phyllochron interval, leaf appearance rate, P1V = vernalization, P1D = photoperiodism, P5 = grain filling dura-
tion, G1 = grains/ear (GPP), grains/m2
(GPSM), G2 = 1000-seed weight, G3 = spike number, affects lateral tiller production
(TPSM).
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Laurila, H. Simulation of spring wheat responses to elevated CO2
and temperature
Model calibration
The CERES-wheat genetic coefficients govern-
ing the phenological development (PHINT, P1V,
P1D, P5) and yield potential (G1, G2, G3) for
cv. Polkka (Table 2) were calibrated with the
RMSD algorithm (Root Mean Square Differ-
ence). The genetic coefficients were calibrated
for the cv. Polkka by minimizing the RMSD be-
tween the simulated and the observed values
(Table 4). The RMSD was calculated according
to Eq. 1 between the observed and simulated
dates (DOY, Day of Year) for phenological de-
velopment and between the observed and simu-
lated grain yields (t ha–1
). The cv. Polkka record-
ed anthesis and maturity dates and measured
grain yield levels from the MTT official variety
trials (1985–1990) were used as calibration data
(Järvi et al. 2000).
n
RMSD = √ ((Σ (d2
))/n–1) (1)
i =1
where √ is square-root, d is difference (observed-
simulated) in days from sowing to anthesis and
from sowing to full maturity in the calibration
of phenological coefficients (PHINT, P1V, P1D
and P5). Parameter d is also used as the grain
yield difference (observed-simulated) (t ha–1
,
15% moisture content) in the calibration of yield
potential coefficients (G1, G2 and G3). Parame-
ter n is the number of experimental sites* years
(35 total) including 4 MTT testing sites: Anjala,
Kokemäki, Mietoinen, Pälkäne and Salo (Sugar
Beet Research Centre) and Tuusula (Hankkija
Plant Breeding Institute) and 6 experiment years
(1985–1990, except Tuusula only 5 years) (Ta-
ble 4). The calibrated coefficients are presented
in Tables 5–6.
The CERES-wheat non-potential model was
calibrated with the MTT soil data (1985–1990)
for clay, sand, silt and organic soils (Table 4).
The non-potential model was used to simulate
the effects of water and nutrient deficiency (ni-
trogen) stresses during the growing season.
Ritchie (1989) has described the modelling of
water stress during growing period in the
CERES-wheat soil submodel. Hanks and Ritch-
ie (1991) have presented detailed nitrogen dy-
namics between soil and plants.
Sensitivity analysis
Sensitivity analysis has been widely applied in
optimization theory and operation research (Fi-
acco 1983, Gal and Greenberg 1996). In crop
models the sensitivity analysis has been applied
Table 4. MTT experimental sites used in the CERES-wheat genetic coefficient calibration with geographical coordinates,
altitude (m), Temp = mean May–September air temperature (°C), Prec = precipitation (mm) 1970–1990, Phot = photoperiod
and sunshine hours (h) at the nearest meteorological stations next to each MTT experimental site.
Site Location Altitude Temp Prec Phot Soil type
(m) (°C) (mm) (h)
Anttila1)
60° 25'N, 24° 50'E 45 13.5 195 18.3 Sandy clay
Anjala 60° 30'N, 26° 50'E 33 13.2 302 18.4 Sandy clay, mould2)
Jokioinen 60° 49'N, 23° 30'E 104 12.7 319 18.5 Heavy clay
Kokemäki 61° 16'N, 22° 15'E 38 12.7 297 18.7 Coarse sand, fine sand
Mietoinen 60° 40'N, 21° 50'E 13 13.1 308 18.4 Pure clay, sandy clay
Pälkäne 61° 25'N, 24° 20'E 103 13.1 319 18.7 Silt2)
Salo 60° 22'N, 23° 06'E 3 13.6 316 18.3 Sandy clay, silty clay
Tammisto1)
60° 16'N, 24° 50'E 45 13.5 295 18.3 Sandy clay
1)
Hankkija Plant Breeding Institute experimental sites in Tuusula (data for cv. Ruso)
2)
Few field observations for silt and organic soil (peat and mould) types
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to study the sensitivity of a response variable
(e.g. grain yield) on the changes of independent
driving variable (e.g. temperature). According to
Thornley and Johnson (1990), a crop model can
be classified as sensitive or insensitive based on
response variable change. A specific model can
be classified as sensitive, if the independent driv-
ing variable is deviated for example by 10 per
cent causing the response variable to change
more than 10 per cent. If the change of response
variable is less than 10 per cent, a model can be
classified as insensitive. The sensitivity analy-
sis was applied in this study to assess the grain
yield sensitivity to temperature and CO2
chang-
es both with potential and non-potential mod-
els.
Results
Calibration of phenological coefficients
The optimum phenological coefficients for cv.
Polkka with RMSD values for anthesis
(RMSDANTH
) and for full maturity (RMSDFMAT
)
under ambient temperature and CO2
conditions
for PHINT, P1V, P1D and P5 were 60.0, 0.1, 1.0,
10.0 respectively (Table 5).
Calibration of yield component coefficients
The yield component coefficients (G1, G2 and
G3) for cv. Polkka with the RMSD values for
grain yield (RMSDYLD
) (Table 6) were calibrat-
ed with the Anjala, Mietoinen, Kokemäki,
Pälkäne and Salo research stations soil data (Ta-
ble 4). In addition, Hankkija (Anttila and Tam-
misto sites) cultivar trial data with long time-
series (1968–1972) for spring wheat (cv. Ruso)
were used. Cv. Ruso resembles cv. Polkka in
phenological development, yield potential and
with yield quality (Peltonen et al. 1990). Both
cv. Ruso and cv. Polkka are late cultivars: 102
(cv. Ruso) versus 102 (cv. Polkka) growing days
from sowing to yellow ripening stage. The aver-
age grain yield is 3770 and 4030 kg/ha, 1000-
seed weight 37.2 and 33.0 g and protein content
14.0 and 14.7 per cent on cv. Ruso and cv. Polk-
ka, respectively (Järvi et al. 2000). The optimum
yield coefficients for G1, G2 and G3 were 5.0,
1.0 and 1.5 respectively with all MTT soil data
pooled together (Table 6).
The optimum genetic coefficients for cv.
Polkka under ambient CO2
and temperature con-
ditions were for PHINT, P1V, P1D, P5, G1, G2,
G3 60.0, 0.1, 1.0, 10.0, 5.0, 1.0, 1.5 respective-
ly.
Model validation and evaluation results
Evaluating phenological development
The cv. Polkka growing days simulated with the
phenological submodel (PHENOL) between
sowing and anthesis dates (Table 7) were on av-
erage 61 days after sowing in ambient versus 63
observed and 51 days in elevated temperature
(+3°C) versus 59 observed average (1992–1994)
(Hakala 1998a). The observed mean anthesis
(1992–1994) occurred on 194 DOY in ambient
conditions. Respectively the simulated anthesis
(sowing 15 May) occurred on 192 DOY with
mean difference of 2 days between observed and
simulated.
Table 5. Phenological coefficients (PHINT, P1V, P1D and P5) for cv. Polkka.
RMSDANTH
RMSDFMAT
PHINT P1V P1D P5
(d) (d) (dd)
2.99 5.86 60.0 0.10 1.00 10.0
RMSDANTH
= RMSD for anthesis (d), the anthesis is reached ca. 5 days after heading
RMSDFMAT
= RMSD for full maturity (d)
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and temperature
The simulated growing days between sow-
ing and full maturity dates were on average 113
days under ambient growing conditions versus
106 observed. Respectively the simulated grow-
ing days were 92 days under elevated tempera-
ture (+3°C) versus 99 days observed. According
to MTT official variety trials, the average grow-
ing day number with cv. Polkka under ambient
Table 7. Simulated (cv. Polkka, potential model) anthesis and full maturity estimates (d) from sowing vs. observed mean
values (SILMU 1992–1994) (Hakala 1998a).
Sowing – anthesis Sowing – full maturity
Observed Simulated Observed Simulated
CO2
DTEMP
(SE) OANTH
(SE) SANTH
DANTH
(SE) OFMT
(SE) SFMT
DFMT
(ppm) (°C) (d) (%) (d) (%) (d) (d) (%) (d)*1)
(%) (d)
350 0 62.62)
(1.9) – 60.73)
(3.0) – 11.90 105.64)
(3.1) – 113.35)
(6.0) –– –7.70
350 3 59.02)
(5.8) –5.75 51.02)
(3.0) –15.98 18.00 199.32)
(3.0) –5.97 191.72)
(1.0) –19.06 –7.60
700 0 62.32)
(1.8) –0.48 60.72)
(3.0) –10.00 11.60 109.72)
(2.0) –3.88 113.32)
(6.0) –10.00 –3.60
700 3 62.32)
(5.4) –0.48 51.02)
(3.0) –15.98 11.30 196.32)
(3.0) –8.81 191.72)
(1.0) –19.06 –4.60
CO2 = CO2 concentration (ppm), DTEMP
= temperature change (°C), SE = standard error of the mean in observed and
simulated values (1992–1994), OANTH
= anthesis change (%) from the observed mean reference (350 ppm/0°C), SANTH
=
anthesis change (%) from the simulated mean reference, OFMT
= full maturity change (%) from the observed mean reference,
SFMT
= full maturity change (%) from simulated mean reference, DANTH
= difference between observed and simulated anthe-
sis (d), DFMT
= difference between observed and full maturity (d)
1)
Full maturity occurs ca. five days after the yellow ripening stage (Järvi et al. 2000).
2)
Reference value for OANTH
3)
Reference value for SANTH
4)
Reference value for OFMT
5)
Reference value for SFMT
Table 6. Yield component coefficients (G1, G2 and G3) for spring wheat (cv. Polkka, Svalöf and cv. Ruso, Jo).
Soil type RMSDYLD
G1 G2 G3
(t/ha)
Sand (coarse and fine)1, 3)
1.7478 0.50 5.00 5.00
Heavy clay1, 4)
1.8323 1.00 8.50 1.00
Mixed clays5)
1.7245 1.00 8.50 1.00
Silt, Silt loam2)
1.4080 1.00 6.00 1.00
Organic soil (Peat, Mould)2)
0.2892 2.00 2.30 2.00
All soil data pooled 1.7980 5.00 1.00 1.50
RMSDYLD
= RMSD for grain yield (t ha–1
).
1)
Contains coarse sand, fine sand and loamy sand soil types
2)
Few observations in MTT official variety trial database, the optimized coefficients are only estimates
3)
Data from Kokemäki (coarse sand, 1986–1990), Kokemäki (fine sand, 1985), Tuusula (fine sand, 1988) MTT experi-
mental stations
4)
Data from Mietoinen (heavy clay, 1986–1988,1990) MTT experimental station
5)
Data from Anjala (sandy clay, silty clay, 1988–1990), Salo (sandy clay, silty clay, 1985–1989), Tuusula (sandy clay, silty
clay, 1985–1987) MTT experimental stations, Hankkija Plant Breeding Institute Anttila and Tammisto experimental
sites (cv. Ruso, 1968–1988)
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conditions is ca. 102 days from sowing to yel-
low maturity stage. The phase from yellow rip-
ening stage to full maturity is ca. five days (Järvi
et al. 2000, Kangas et al. 2001). The growing
period (d) between sowing and full maturity was
104 /1992, 113/1993 and 100/1994 days in am-
bient CO2
and temperature.
Validation of yield components
During the validation of the potential and non-
potential models, the simulated grain yield,
above ground biomass and harvest index (HI)
estimates were compared with the mean OTC
experiment values (1992–1994) (Hakala 1998a).
The simulated grain yield estimates (potential
and non-potential models) versus observed mean
values are presented in Table 8. Both the abso-
lute and percentage differences between ob-
served and simulated estimates are tabulated.
Respectively the simulated estimates and ob-
served mean values for biomass and HI are pre-
sented in Table 9. In addition, other significant
yield components (1000 seed-weight, grains/ear
and tillers/m2
) were taken into account (Table
10).
The observed mean grain yield (1992–1994)
was 5.47 t ha–1
under ambient conditions (Haka-
la 1998a). The potential model (sowing 15 May)
overestimated the grain yield (6.16 t ha–1
) with
mean difference of 0.69 t ha–1
(DPYIELD
) (12.6%,
PPYIELD
) between observed and simulated. Re-
spectively the non-potential model (sowing
15 May) underestimated the grain yield (4.49 t
ha–1
) with mean difference of 0.98 t ha–1
(DNYIELD
) (17.9%, NPYIELD
) (Table 8).
The observed mean grain yield (1992–1994)
was 4.62 t ha–1
under elevated temperature con-
ditions (3°C). The observed grain yield with el-
evated temperature was 17% (OYIELD
) lower com-
pared to the ambient grain yield. Respectively
the simulated grain yield with the potential mod-
el was 4.05 t ha–1
. The simulated grain yield with
potential model under elevated temperature was
19% (SPYIELD
) lower compared to the simulated
ambient grain yield. The potential model under-
estimated under elevated temperature the grain
yield by 570 kg ha–1
(DPYIELD
) (12.3%, PPYIELD
)
compared with the observed. Respectively the
simulated grain yield with non-potential model
was 23% (SNYIELD
) lower compared to the simu-
Table 8. Simulated (cv. Polkka, potential and non-potential models) mean grain yield (t ha–1
) vs. observed mean values
(SILMU 1992–1994) (Hakala 1998a).
Grain yield 1)
Observed Potential model Non-potential model
CO2
DTEMP
(t ha–1
) OYIELD
(t ha–1
) SPYIELD
DPYIELD
PPYIELD
(t ha–1
) SNYIELD
DNYIELD
NPYIELD
(ppm) (°C) (SE) (%) (SE) (%) (t ha–1
) (%) (%) (t ha–1
) (%)
350 0 5.47 (0.6)2)
– 6.16 (1.0)3)
– –0.69 –12.61 4.494)
– –0.98 –17.92
350 3 4.62 (0.4)2)
–17.09 4.05 (0.4)2)
–19.64 –0.57 –12.34 3.492)
–22.27 –1.13 –24.50
700 0 6.15 (0.9)2)
–12.43 8.77 (1.5)2)
–42.37 –2.62 –42.60 7.522)
–67.48 –1.37 –22.28
700 3 5.54 (0.2)2)
1–1.28 6.56 (0.8)2)
–16.49 –1.02 –18.41 5.522)
–22.94 –0.02 1–0.36
CO2
= CO2
concentration (ppm), DTEMP
= temperature change (°C), SE = standard error of the mean in observed and
simulated values (1992–1994), OYIELD
= grain yield change (%) from the observed mean reference (350 ppm/0°C), SPYIELD
= grain yield change (%) from the simulated mean reference (potential), DPYIELD
= difference (t ha–1
) between observed and
simulated grain yield (potential), PPYIELD
= simulated grain yield difference (%) from the observed (potential), SNYIELD
=
grain yield change (%) from the simulated mean reference (non-potential), DNYIELD
= difference (t ha–1
) between observed
and simulated grain yield (non-potential), NPYIELD
= simulated grain yield difference (%) from the observed (non-potential).
1)
15% moisture grain yield content
2)
Reference value for OYIELD,
3)
Reference value for SPYIELD,
4)
Reference value for SNYIELD
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Laurila, H. Simulation of spring wheat responses to elevated CO2
and temperature
lated ambient grain yield. The non-potential
model underestimated with elevated temperature
the grain yield by 1.13 t ha–1
(DNYIELD
) (24.5%,
NPYIELD
) compared with the observed.
The observed mean grain yield (1992–1994)
was 6.15 t ha–1
under elevated CO2
conditions
(CO2
700 ppm, +0°C). The observed grain yield
with elevated CO2
was 12% (OYIELD
) higher com-
pared to the ambient grain yield. The simulated
grain yield with potential model with elevated
CO2
was 8.77 t ha–1
. Respectively the simulated
grain yield with elevated CO2
was 42% (SPYIELD
)
higher compared to the ambient simulated yield.
The potential model overestimated under elevat-
ed CO2
conditions the grain yield by 2.6 t ha–1
(DPYIELD
) (42%, PPYIELD
) compared with the ob-
served. Respectively the simulated grain yield
with the non-potential model was 7.52 t ha–1
. The
simulated grain yield with elevated CO2
was 67%
(SNYIELD
) higher compared to the ambient simu-
lated yield. The non-potential model overesti-
mated under elevated CO2
conditions the grain
yield by 1.37 t ha–1
(DNYIELD
) (22%, NPYIELD
)
compared with the observed (Table 8).
The observed mean grain yield (1992–1994)
was 5.54 t ha–1
under elevated CO2
and tempera-
ture conditions (CO2
700 ppm, +3°C). The ob-
served grain yield with elevated CO2
and tem-
perature was only 1.3 per cent (OYIELD
) higher
compared to the ambient grain yield. The simu-
lated grain yield with the potential model was
6.56 t ha–1
. Respectively the simulated grain yield
with elevated CO2
and temperature was 6.49%
(SPYIELD
) higher compared to the ambient sim-
ulated yield (sowing 15 May). The potential
model clearly overestimated under elevated CO2
and temperature conditions the grain yield by
1.02 t ha–1
(DPYIELD
) (18.4%, PPYIELD
) compared
with the observed. Respectively the simulated
grain yield with non-potential model was 5.52 t
ha–1
. The simulated grain yield (non-potential
model) under elevated CO2
and temperature was
22.9% (SNYIELD
) higher compared to the ambi-
ent simulated yield (sowing 15 May). The non-
potential model predicted accurately under ele-
vated CO2
and temperature the grain yield
(DNYIELD
=20 kg ha–1
) (0.4%, NPYIELD
) compared
with the observed (Table 8).
The potential model simulated HI relatively
accurately only under ambient temperature and
CO2
conditions. However, the HI difference (DHI
)
between observed and simulated deviated more
than 20% under elevated temperature CO2
con-
ditions (Table 9). The observed mean HI (1992–
1994) was 0.440 (0.503 simulated) in ambient
conditions. The observed HI was 0.380 (0.505
simulated) in elevated temperature (+3°C). The
observed HI was 0.420 (0.501 simulated) in ele-
vated CO2
(CO2
700 ppm). Respectively the ob-
served HI was 0.370 (0.493 simulated) in ele-
Table 9. Simulated (cv. Polkka, potential model) above ground biomass and harvest index (HI) values vs. observed mean
values (SILMU 1992–1994) (Hakala 1998a).
CO2
DTEMP
Above ground biomass1)
Harvest Index (HI)
(ppm) (°C)
Observed Simulated DABGR
Observed Simulated DHI
(SE) (SE)
(t ha–1
) (t ha–1
) (%) (%) (%) (%)
350 0 12.22 12.06 (1.2) 1–1.31 0.440 0.503 (0.043) 14.32
350 3 11.96 18.10 (0.7) –32.27 0.380 0.505 (0.052) 32.89
700 0 14.33 17.22 (1.7) –20.17 0.420 0.501 (0.044) 19.29
700 3 14.75 13.27 (0.6) –10.03 0.370 0.493 (0.044) 33.24
CO2
= CO2
concentration (ppm), DTEMP
= temperature change (°C), DABGR
= simulated above ground biomass difference (%)
from the observed (potential model), DHI
= simulated HI difference (%) from the observed Harvest Index (potential model),
SE = standard error of the mean in observed and simulated values (1992–1994).
1)
15% moisture content
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Vol. 10 (2001): 175–196.
vated CO2
and temperature (CO2
700 ppm,
+3°C). The potential model simulated above
ground biomass accurately only under ambient
temperature and CO2
conditions. However, the
above ground biomass difference (DABGR
) be-
tween observed and simulated was more than 30
per cent under elevated temperature conditions
(Table 9).
The simulated and observed yield compo-
nents (1000-seed weight (g), grains/ear, grains/
m2
and tillers/m2
) are presented in Table 10. The
potential model simulated 1000-seed weight rel-
atively accurately, only with elevated CO2
the
difference (DSWG
) between observed versus sim-
ulated deviated more than 10%. Respectively the
tillers/m2
difference (DTLL
) remained below 15%
level. However, the grains/ear difference (DGRE
)
was significant (35%) under elevated tempera-
ture and CO2
conditions.
Sensitivity analysis results
The grain yield sensitivity for temperature and
CO2
changes was analysed with both potential
and non-potential models (Table 11). The applied
dichotomy classification (sensitive/insensitive)
is after France and Thornley (1984), Thornley
and Johnson (1990). According to sensitivity
analysis results, both the potential and non-po-
tential models were sensitive to small tempera-
ture changes in mean temperature. Only with the
non-potential model, the temperature increase of
20 per cent (equal to +3°C increase) decreased
the grain yield less than corresponding tempera-
ture change. When analysing the CO2
sensitivi-
ty results only the potential model was sensitive
to CO2
deviations below 20 per cent (450 ppm),
in higher CO2
concentrations both potential and
non-potential models were insensitive. Respec-
tively the potential model was sensitive to con-
current CO2
and temperature changes below 20
per cent (400 ppm and +2°C). However, in high-
er CO2
and temperature levels both the potential
and non-potential models were insensitive.
Elevated CO2
and temperature effects under
potential growing conditions
The sensitivity analysis results for elevated CO2
effect indicate that the elevated CO2
concentra-
tion increased the biomass and yield potential
of cv. Polkka from CO2
compensation point (ca.
50 ppm) to saturation point (ca. 1000 ppm) (Law-
lor 1987, Lawlor et al. 1989, Hakala et al. 1999).
According to the sensitivity analysis results for
cv. Polkka potential yield, the grain yield in-
creased with potential model to +142% (8.77 t
ha–1
) under elevated CO2
conditions (Point D,
Fig. 1) from the ambient simulated reference
(100%, 6.2 t ha–1
) (Point A, Fig. 1). The 100%
baseline of yield reference with isoline of equal
yield refers to current ambient temperature and
Table 10. Simulated (cv. Polkka, potential model mean yield component values vs. observed mean values (SILMU 1992–
1994) (Hakala 1998a).
CO2
DTEMP
1000-seed weight Grains/ear Tillers/m2
(ppm) (°C) (g)
Obs. Sim. DSWG
Obs. Sim. DGRE
Obs. Sim DTLL
(SE) (%) (SE) (%). (SE) (%)
350 0 34.4 37.1 (2.9) 17.85 22.2 23.7 (2.1) 16.76 615.7 584.4 (16.9) 1–5.08
350 3 32.7 32.7 (2.8) 10.00 19.2 18.7 (1.1) –2.60 649.0 565.5 (<1) –12.87
700 0 33.4 37.8 (3.3) 13.17 23.5 26.9 (1.6) 14.47 643.1 718.4 (36.4) –11.71
700 3 33.9 32.7 (2.8) –3.54 21.2 28.6 (1.4) 34.91 701.9 592.9 (3.9)1 –15.53
CO2
= CO2
level (ppm), DTEMP
= Temperature change (°C), DSWG
= simulated 1000-seed weight difference (%) from the
observed, DGRE
= simulated grains/ear difference (%) from the observed, DTLL
= simulated tillers/m2
difference (%) from the
observed, SE = standard error of the mean in observed and simulated values (1992–1994).
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Laurila, H. Simulation of spring wheat responses to elevated CO2
and temperature
CO2
level. Respectively the measured mean grain
yield (1992–1994) increased to 112% (6.15 t
ha–1
) from the ambient reference level (5.47 t
ha–1
) (Hakala 1998a).
The simulation results for elevated tempera-
ture effect indicated a clear acceleration of phe-
nological development between anthesis and full
maturity and a decrease of grain yield and above
ground biomass. Especially after the anthesis, the
ripening of the grains was accelerated through
the increase of thermal time. Full maturity was
thus reached earlier, causing a reduction in the
final grain yield. The potential model decreased
the grain yield to 80.4% (4.1 t ha–1
) (Point B,
Fig. 1) under elevated temperature conditions
from ambient simulated reference (100%, 6.2 t
ha–1
). Respectively the measured mean yield
(1992–1994) decreased to 84 per cent (4.62 t
ha–1
) from the ambient reference (Hakala 1998a).
The simulation results for elevated tempera-
ture and CO2
interaction indicate that the increase
in biomass and grain yield due to the elevated
CO2
was reduced through the interaction with
elevated temperatures. The potential model in-
creased the grain yield to 106% (6.56 t ha–1
, Point
C, Fig. 1) from the simulated ambient reference
(6.2 t ha–1
). Respectively the measured mean
grain yield (1992–1994) increased to 102% (5.54
t ha–1
) from the observed ambient reference
(Hakala 1998a).
Non-potential growing conditions
The sensitivity analysis results under non-opti-
mal growing conditions (water and nutrient de-
Table 11. The sensitivity analysis results for potential and non-potential models: the grain yield sensitivity
(%) of cv. Polkka on different temperature and CO2
deviations (%).
Driving variable Response variable (grain yield, t ha–1
)
Potential model4)
Non-potential model4)
Change Yield Sensitivity Yield Sensitivity
(%)5)
change (%) class change (%) class
Temperature Temperature
change (°C)1)
1 117 –19.0 Sen –15.7 Sen
2 113 –22.9 Sen –19.3 Sen
32)
120 –22.7 Sen –13.3 Ins
CO2
CO2
-level
(ppm)
390 111 –11.9 Sen –19.6 Ins
438 121 –32.7 Sen –11.4 Ins
525 150 –38.3 Ins –32.8 Ins
7003)
100 –63.9 Ins –67.4 Ins
CO2
* temperature CO2
/
Temp.
390/2 11–13 –39.5 Sen 1–0.7 Ins
440/3 20–21 –13.6 Ins –12.2 Ins
Response variable (grain yield) dichotomy classification: Sen = Sensitive, Ins = Insensitive
1)
The mean reference temperature is ca. 10–15°C during the growing season in Southern Finland (Hakala
1998a)
2)
Temperature level corresponds to the point B (point A reference) in Fig. 1. and Fig. 2.
3)
CO2
level corresponds to the point D (point A reference) in Fig. 1. and Fig. 2.
4)
Negative percentage change denotes decreasing yield
5)
Percentage change is calculated from the current ambient temperature and CO2
.
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Vol. 10 (2001): 175–196.
ficiencies during growing period) are presented
in Fig. 2. The sensitivity analysis results indi-
cate that, under elevated CO2
and temperature
condition (700 ppm CO2
/3°C) the grain yield in-
creased to +122% (5.52 t ha–1
, Point C, Fig. 2)
from the reference (Point A, Fig. 2) (100%, 4.49
t ha–1
). The simulated grain yield level increased
to +167 percentage under elevated CO2
conditions
(7.52 t ha–1
, Point D, Fig. 2) from the simulated
ambient reference. However, the simulated grain
yield decreased under elevated temperature to
–76.8 percentage (3.49 t ha–1
, Point B, Fig. 2).
Impact assessment
Impact assessment of early sowing on wheat
phenology and yield potential
According to simulation results, the observed
mean anthesis occurred on 167 DOY (Table 12)
Fig. 1. Results of the sensitivity analysis of the CERES-wheat potential model for grain yield (t ha–1
) of spring wheat cv.
Polkka in response to CO2
(ppm) and temperature (°C). Model reference values are 0°C and 350 ppm (point A) indicating
change from current mean temperature and CO2
level. Isolines denote mean grain yield change (%) with steps of ±25%
from the reference (100%) going through point A.
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A G R I C U L T U R A L A N D F O O D S C I E N C E I N F I N L A N D
Laurila, H. Simulation of spring wheat responses to elevated CO2
and temperature
with earlier sowing (15 d, sowing 29 April 1992)
under elevated temperature conditions (Hakala
1998a). Respectively the simulated anthesis oc-
curred on 171 DOY with mean difference of 4
days between observed and simulated (Table 12).
The observed anthesis with earlier sowing (15 d)
and elevated temperature occurred 27 days ear-
lier compared to the reference anthesis date (15
July). Respectively the simulated anthesis with
earlier sowing (15 d) occurred 18 days earlier
compared to the simulated reference anthesis
date. The potential model estimated the anthesis
to occur with earlier sowing on average 9 days
later compared with the observed.
The observed mean full maturity (1992–
1994) occurred on 236 DOY in ambient condi-
tions. Respectively the simulated full maturity
(sowing 15 May) occurred on 244 DOY with
Fig. 2. Results of the sensitivity analysis of the CERES-wheat non-potential-model (with stress factors: water stress, nitro-
gen deficiency) for grain yield (t ha–1
) of spring wheat cv. Polkka in response to CO2
(ppm) and temperature (°C). Model
reference values are 0°C and 350 ppm (point A) indicating change from current mean temperature and CO2
level. Isolines
denote grain yield change (%) with steps of ±25 % from the reference (100%) going through point A.
15. 189
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mean difference of 8 days between observed and
simulated. The observed mean full maturity oc-
curred on 209 DOY with earlier sowing (15 d)
under elevated temperature condition (sowing 29
April 1992). Respectively the simulated full
maturity occurred on 213 DOY with mean dif-
ference of 4 days between observed and simu-
lated. The observed full maturity with earlier
sowing (15 d) under elevated temperature oc-
curred 32 days earlier compared to the reference
full maturity date (22 August) in ambient condi-
tions (Table 12). Respectively the simulated full
maturity with earlier sowing (15 d) occurred 31
days earlier compared to the reference full ma-
turity. The potential model estimated the full
maturity to occur with earlier sowing on aver-
age one day later compared with the observed.
According to MTT variety trials, the cv. Polkka
full maturity occurs on average 5 days from the
yellow ripening stage (Järvi et al. 2000, Kangas
et al. 2001).
The observed mean above ground biomass
(1992–1994) was 12.22 t ha–1
(9.7 t ha–1
simulat-
ed) in ambient conditions (Table 13). Respec-
tively the observed mean above ground biomass
was 12.12 t ha–1
(10.5 t ha–1
simulated) with ear-
lier sowing (15 d, sowing 29 April) under ele-
vated temperature condition.
The observed mean 1000-seed weight (1992–
1994) was 34.4 g (35.4 g simulated) in ambient
conditions. Respectively the observed 1000-seed
weight was 36.9 g (37.0 g simulated) with earli-
er sowing (15 d, sowing 29 April) and elevated
temperature. The observed mean grains/ear var-
iable (1992–1994) was 22.2 g (18.9 g simulat-
ed) in ambient conditions. Respectively the ob-
served grains/ear variable was 24.6 g (22.7 g sim-
ulated) with earlier sowing (15 d, sowing 29
April) and elevated temperature (Table 13).
The observed mean grain yield was 4.95 t
ha–1
with earlier sowing (15 d) under elevated
temperature conditions (sowing 29 April 1992,
Table 12. Simulated results (potential model) of earlier sowing for cv. Polkka phenology vs. observed
values (SILMU 1992–1994) (Hakala 1998a).
CO2
DTEMP
SOW OANTH
SANTH
DANTH
OFMT
SFMT
DFMT
(ppm) (°C) (d) (DOY) (DOY) (d) (DOY) (DOY) (d)
350 Ref.1)
01)
01)
194 192 2 236 244 –8
350 3 0 190 182 8 234 226 –8
350 3 10 175 216
350 0 15 182 230
350 3 15 1672)
171 –4 2092)
213 –4
350 5 15 165 205
700 0 0 192 190 2 234 238 –4
700 3 0 170 159 11 207 202 –5
700 3 5 178 219
700 3 10 175 216
700 0 15 182 230
700 3 15 171 213
700 5 15 165 205
The date of 15 May used as the sowing reference value. SOW = earlier sowing (number of days before
15 May), CO2
= CO2
concentration (ppm), DTEMP
= temperature change (°C), OANTH
= observed anthesis
date (DOY, SANTH
= simulated), DANTH
= difference between observed anthesis vs. simulated (d), OFMT
=
observed full maturity date (DOY, SFMT
= simulated), DFMT
= difference between observed full maturity vs.
simulated (d).
1)
Used as the reference value (sowing 15 May, CO2
350 ppm, ambient temperature)
2)
Observed 1992 mean value from OTC experiment (sowing 29 April, CO2
350 ppm, +3°C) (Hakala
1998a)
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Laurila, H. Simulation of spring wheat responses to elevated CO2
and temperature
3°C) (Table 14). Respectively the simulated
grain yield was 5.6 t ha–1
. The observed grain
yield with earlier sowing (15 d) and elevated
temperature was 9.5% lower compared to the
ambient grain yield. Respectively the simulated
grain yield with earlier sowing (15 d) was 19
per cent higher compared to the simulated am-
bient grain yield (sowing 15 May). The poten-
tial model overestimated with earlier sowing the
grain yield by 650 kg ha–1
(13%) compared to
the observed (sowing 29 April 1992, +3°C).
The simulated grain yield under elevated CO2
and temperature conditions was 40% higher com-
pared to the ambient simulated yield (sowing 15
May). The model clearly overestimated with el-
evated CO2
and temperature conditions the grain
yield by 1.02 t ha–1
(18.4%) compared with the
observed. Respectively the simulated grain yield
was 8.40 t ha–1
in earlier sowing (15 d) and ele-
vated CO2
and temperature (CO2
700 ppm,
+3°C), the yield increase was 78% from the
ambient simulated reference (Table 14).
Discussion
Spring wheat phenological development
The simulation results indicate that the phenol-
ogy submodel estimated relatively accurately the
phenological development of cv. Polkka. The
anthesis and full maturity estimates for cv. Polk-
ka deviated less than one week versus observed
mean values in OTC experiments (1992–1994).
Only with elevated temperature and CO2
, the
simulated anthesis difference vs. observed was
11 days. Respectively the simulated full maturi-
ty difference was less than one week.
Table 13. Simulated results (potential model) of earlier sowing for cv. Polkka biomass and yield components vs. observed
values (SILMU 1992–1994) (Hakala 1998a).
CO2
DTEMP
SOW OBMASS
SBMASS
OKERWT
SKERWT
OGPP
SGPP
STPSM
(ppm) (°C) (d) (t ha–1
) (t ha–1
) (g) (g) (grains/ear) (grains/ear) (tillers/m2
)
350 Ref.1)
01)
01)
12.22 9.7 34.4 35.4 22.2 18.9 574.5
350 3 0 11.96 – 32.7 – 19.2 – –
350 3 5 9.5 36.9 20.8 565.5
350 3 10 10.7 36.6 23.0 565.5
350 0 15 14.0 37.9 28.1 612.0
350 3 15 12.122)
10.5 36.92)
37.0 24.62)
22.7 565.5
700 0 0 14.33 – 33.4 – 23.5 – –
700 0 5 18.4 39.9 28.1 750.5
700 3 0 14.75 – 33.9 – 21.2 – –
700 3 5 14.7 37.5 28.5 624.9
700 3 10 15.8 36.9 28.1 669.2
700 0 15 19.6 38.6 30.8 768.3
700 3 15 15.8 37.3 29.0 652.6
700 5 15 13.5 36.9 27.4 580.1
The date of 15 May used as the sowing reference value. SOW = earlier sowing (number of days before 15 May), CO2
= CO2
concentration (ppm), DTEMP
= temperature change (°C), OBMASS
= observed above ground biomass (t/ha, (SBMASS
= simulated
biomass), OKERWT
= observed 1000-seed weight (g, SKERWT
= simulated), OGPP
= observed grains/ear in main shoot (SGPP
=
simulated), STPSM
= simulated tillers/m2
.
1)
Used as the reference value (sowing 15 May, CO2
350 ppm, ambient temperature)
2)
Observed 1992 mean value from OTC experiment (sowing 29 April, CO2
350 ppm, +3°C) (Hakala 1998a).
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Vol. 10 (2001): 175–196.
Under elevated temperature, the potential
model accelerated the phenological development
from sowing to anthesis and from sowing to full
maturity. Both in vegetative phase before the
anthesis and in generative phase from anthesis
to full maturity the potential model estimated the
phenological phases to occur too early under el-
evated temperature conditions compared with the
observed mean values (1992–1994). This might
be explained by low phyllochron value (60 dd)
used in simulation for cv. Polkka compared to
default value (95 dd) for spring wheat genotypes
cultivated in Northern Europe (Table 3). In ad-
dition, the grain filling duration variable P5 was
high (10.0) compared to default value (2.5) and
the threshold temperature (Tb
) for different
growth stages (Table 1) was low (0–2°C) com-
pared to previous results suggesting 4.0°C for
vegetative and 8.0°C for generative phase (Kont-
turi 1979). However, under ambient and elevat-
ed CO2
conditions, the potential model predict-
ed the anthesis accurately. In addition, the feed-
back mechanism between the CO2
and phenolog-
ical subroutines in the potential model (CERES-
wheat v. 1.9) accelerated the phenological de-
velopment under elevated CO2
and temperature
conditions. The observed yellow ripening dates
under elevated temperature and CO2
conditions
confirm this phenomenon (Hakala 1998a).
Table 14. The simulated (cv. Polkka, potential model) results of earlier sowing for grain yield (t ha–1
, 15% moisture content)
vs. observed mean grain yields (SILMU 1992–1994) (Hakala 1998a).
Observed Simulated Difference (obs.-sim.)
CO2 DTEMP
SOW OYIELD
(SE) POYIELD
SYIELD
PSYIELD
DYIELD
DDIF
(ppm) (°C) (d) (t ha–1
) (%) (t ha–1
) (%) (t ha–1
) (%)
350 Ref1)
0 0 5.47 (0.6)3)
– 4.704)
– –0.77 –14.08
350 3 0 4.62 (0.4) 3)
–15.54 4.055)
–13.83 –0.57 –12.34
350 3 5 5.105)
– 8.51
350 3 10 5.605)
–19.15
350 0 15 7.705)
–63.83
350 3 15 4.952)
–9.51 5.605)
–19.15 –0.65 –13.13
350 5 15 4.305)
–8.51
700 0 0 6.15 –12.43 8.775)
–86.60 –2.62 –42.60
700 3 0 5.54 –1.28 6.565)
–39.57 –1.02 –18.41
700 3 5 7.905)
–68.09
700 3 10 8.205)
–74.47
700 0 15 10.80 4)
129.79
700 3 15 8.405)
–78.72
700 5 15 6.905)
–46.81
The date of 15 May used as the sowing reference value. CO2
= CO2
concentration (ppm), DTEMP
= temperature change (°C),
SOW = earlier sowing (d) before reference sowing date (15 May), OYIELD
= observed mean grain yield (t ha–1
), POYIELD
=
observed grain yield change (%) from the observed mean reference, SYIELD
= simulated grain yield estimate (t ha–1
), PSYIELD
= simulated grain yield change (%) from the simulated mean reference, DYIELD
= difference between observed and simulated
yield (t ha–1
), DDIF
= simulated grain yield difference (%) from the observed, SE = standard error of the mean (1992–1994).
1)
Ref. used as the reference value (sowing 15 May, CO2 350 ppm, ambient temperature)
2)
Observed 1992 mean value from OTC experiment (sowing 29 April, CO2 350 ppm, +3°C) (Hakala 1998a).
3)
Reference value for POYIELD,
4)
Reference value for PSYIELD,
5)
Simulated mean grain yield estimate (1992–1994)
18. 192
A G R I C U L T U R A L A N D F O O D S C I E N C E I N F I N L A N D
Laurila, H. Simulation of spring wheat responses to elevated CO2
and temperature
Grain yield and other yield components
under elevated temperature and CO2
The mean observed grain yield for cv. Polkka
under ambient growing conditions versus simu-
lated estimates with potential and non-potential
models suggest that the growing conditions
might have been sub-optimal under ambient con-
ditions in OTC experiments (1992–1994). The
observed mean grain yield remained between the
potential and non-potential estimates. Recent
studies have critically reviewed problems with
the data from OTC experiments. van Oijen et al.
(1999) suggested that OTC experiments might
overestimate the effects of rising CO2
with spring
wheat genotypes. Moreover, several recent pub-
lications have critically reviewed the validation
results of crop simulation models with CO2
and
O3
(ozone) data from OTC (van Oijen and Ew-
ert 1999) and free-air CO2
enrichment (FACE)
experiments. Tubiello et al. (1999) validated the
CERES-wheat model with FACE data. Ewert et
al. (1999) published revised modelling results
with CO2
and O3
data for spring wheat growth
and development in different sites in Europe. In
addition, the CERES-wheat model was originally
developed for the simulation of field conditions
(Ritchie and Otter 1985, Hanks and Ritchie
1991), which differ from OTC growing condi-
tions.
Both the potential and non-potential models
underestimated the grain yield under elevated
temperature conditions versus observed mean
grain yields. This might imply that the pheno-
logical submodel terminated the grain filling
phase too early. According to the CERES-wheat
model estimations, the yield of cv. Polkka de-
creased on average to 80.4 % with potential
model (76.8% with non-potential simulating
water and nitrogen deficiencies) under elevated
temperature conditions compared to the simu-
lated reference (100%).
Both the potential and non-potential models
overestimated the grain yield under elevated CO2
conditions versus observed. This might imply
that the coefficients for CO2
response in the
CERES-wheat model (version 1.9.) overestimat-
ed the CO2
response on cv. Polkka grain yield.
The yield of cv. Polkka increased to 142% un-
der elevated CO2
condition with potential mod-
el (167% with non-potential) from the simulat-
ed reference (100%). Respectively the observed
grain yield increased only to 112 per cent. Orig-
inally the SILMU OTC experiment was estab-
lished to mimic the potential growing conditions
for cv. Polkka under both ambient and elevated
temperature and CO2
conditions (Hakala 1998a).
However, several previous studies have suggest-
ed that C3
-metabolic pathway plants will increase
the grain yield potential between 20 to 53% un-
der doubled CO2
concentration (Kimball 1983,
Goudriaan et al. 1990, van de Geijn et al. 1993).
However, the forecasted variation range is very
large (Cure and Acock 1986). In that respect, the
simulated grain yields under elevated CO2
in this
study accord with the projected range.
The potential model clearly overestimated the
grain yield under elevated temperature and CO2
conditions versus observed mean grain yield.
When simulating the interactive effect of in-
creased CO2
and temperature together, the in-
crease in grain yield due to elevated CO2
was
reduced by the elevated temperature producing
a net increase between 6–22%. The grain yield
increased with the potential model on average
to 106% (122% non-potential) from the simu-
lated reference yield. In addition, the yield com-
ponent grains per ear difference between ob-
served and simulated was significant (35%) with
the potential model under elevated temperature
and CO2
conditions. This might indicate that the
potential model is sensitive to wheat transloca-
tion changes during grain filling period in yel-
low ripening stage before full maturity (growth
stages 5 and 6, Table 1), since all yield compo-
nents are interconnected through the plant me-
tabolism and translocation of assimilates.
However, the non-potential model predicted
accurately the grain yield compared to the ob-
served (Table 8): This might imply that the av-
erage growing conditions under elevated temper-
ature and CO2
in OTC experiments resembled the
sub-optimal conditions simulated with the non-
19. 193
A G R I C U L T U R A L A N D F O O D S C I E N C E I N F I N L A N D
Vol. 10 (2001): 175–196.
potential model. These factors might explain to
some extent the difference between the observed
and simulated grain yields. In addition, one can
speculate how representative the validation data
of only three years (1992–1994) actually is.
Early sowing
If the mean temperature will increase during late
winter and spring in Finland according to cli-
mate change scenarios, it will enable earlier sow-
ing in southern and mid-Finland. In Finland a
longer growing season for crops (ca. 1 month)
is projected: sowing will occur earlier, thus caus-
ing changes in pre-anthesis radiation and day-
length conditions especially during vegetative
phase. Earlier sowing will potentially entail
changes in spring wheat phenological develop-
ment (Saarikko and Carter 1996, Saarikko 1999).
The simulation results with current Finnish
cultivars imply that by using earlier sowing a
substantial increase in grain yields can be ex-
pected under elevated CO2
growing conditions.
Earlier sowing combined with elevated CO2
can
mitigate the decreasing effect of elevated tem-
perature on yield potential. When simulating the
effects of early sowing, the potential model in-
creased the grain yield with earlier sowing dates
(varying between 5 to 15 days) compared to
ambient conditions with mean sowing date in
southern Finland. According to simulation results
with the potential model, the grain yield increase
with earlier sowing days from the reference (15
May) was +51% (5 d), +57% (10 d), +64% (15 d)
under ambient temperature and CO2
conditions.
Respectively under elevated temperature and
CO2
conditions, the simulated grain yield in-
crease with earlier sowing was +68% (5 d), +75%
(10 d), +78% (15 d). The simulation results also
indicate with earlier sowing that the increasing
effect of elevated CO2
on grain yield is reduced
under elevated temperature levels.
There was a large variation in observed grain
yields between years (1992–1994) especially in
the OTC experiments under elevated CO2
con-
ditions (Hakala 1998a). In addition, the meas-
ured earlier sowing data (15 d, sowing 29 April)
with elevated temperature consisted of only 1992
data. The data of only one-year might mitigate
the conclusions drawn between the simulated
grain yield estimates with earlier sowing versus
observed values.
Adaptation strategies for the
climate change
According to climate change scenarios, the fu-
ture climate in Finland (2050–2100) will resem-
ble the growing conditions currently prevailing
in southern Sweden and northern Germany. A
scheme of transferring mid-European cultivars
to Finland to be grown under elevated tempera-
ture and CO2
growing conditions can be hypoth-
esised. A longer growing period would enable
cultivation of crops with a longer growing peri-
od (Carter 1992), supporting the hypothesis of
transferring mid-European spring wheat geno-
types to Finland for cultivation. However, cur-
rent mid-European cultivars are adapted to long-
er growing period and shorter daylength com-
pared with the cultivars currently cultivated in
Finland and adapted to northern long-day grow-
ing conditions. Daylength and photoperiodic
constraints should be evaluated before introduc-
ing mid-European cultivars for cultivation in
Finland. Laurila (1995) evaluated with the
CERES-wheat model phenological development
and yield potential differences between a Ger-
man cultivar (cv. Nandu) and cv. Polkka current-
ly cultivated in Finland under ambient and ele-
vated temperature and CO2
growing conditions.
Simulation results suggested that the mid-Euro-
pean cv. Nandu would benefit more from the el-
evated CO2
and temperature levels. The grain
yield of cv. Nandu increased to 161% versus
158% for cv. Polkka under elevated CO2
condi-
tion (700 ppm) from the ambient reference
(100%). Respectively the grain yield of cv. Nan-
du decreased to 59% versus 57% for cv. Polkka
under elevated temperature conditions, since the
elevated temperature (3°C) accelerated the phe-
nological development with both cultivars. Re-
20. 194
A G R I C U L T U R A L A N D F O O D S C I E N C E I N F I N L A N D
Laurila, H. Simulation of spring wheat responses to elevated CO2
and temperature
spectively the concurrent elevated CO2
and tem-
perature conditions increased the grain yield of
cv. Nandu to 107% versus 104% for cv. Polkka
compared to the ambient reference.
Conclusions
Previous crop physiological experiments and
theoretical calculations suggest, that the yield
potential of wheat cultivars can be increased
even by 30 per cent from the maximum yielding
capacity by using higher radiation levels and
providing that the translocation of assimilates
and the sink capacity are non-limiting factors.
According to theoretical calculations, the maxi-
mum yielding capacity arises above 10 tons per
hectare for wheat cultivars (Stoy 1966, Evans
1973, Evans and Wardlaw 1976). If the doubled
CO2
increases the yield levels of spring wheat
cultivars between 10 and 40 per cent from the
current average yield level, as the crop physio-
logical and simulation results suggest, the yield
levels with current cultivars still remain below
the maximum yielding capacity.
In conclusion, the simulation results suggest
that by using earlier sowing a substantial increase
in grain yields can be expected under elevated
CO2
growing conditions with cultivars currently
cultivated in Finland. Earlier sowing with elevat-
ed CO2
will mitigate the decreasing effect of ele-
vated temperature on grain yields. A longer grow-
ing period due to climate change will potentially
enable cultivation of new wheat cultivars adapt-
ed to a longer growing period: In future it might
be possible to cultivate cultivars currently grown
in central Europe also in Finland.
Acknowledgments. I wish to express my sincere gratitude
to professor Timo Mela, Dr. Kaija Hakala, Ms.Sci. Timo
Kaukoranta, Ms.Sci. Markku Kontturi and Dr. Riitta Saa-
rikko (MTT), professor Timothy Carter (The Finnish Envi-
ronment Institute), professors Eero Varis, Eija Pehu and
Pirjo Mäkelä (Helsinki University), professor Risto Kuit-
tinen (The Finnish Geodetic Institute), professor Tuomo
Karvonen (Helsinki University of Technology), Dr. Jouko
Kleemola (Kemira Agro Oy), professor Jan Goudriaan
(WageningenAgricultural University, Netherlands), profes-
sor John T. Ritchie (The Michigan State University, USA),
Dr. John H.M. Thornley (Hurley Research Station, Berk-
shire, UK) and professor John R. Porter (The Royal Veter-
inary and Agricultural University, Denmark) for their ad-
vises during the modelling work and preparation of the
manuscript.
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SELOSTUS
Kohotettujen CO2
:n ja lämpötilan vaikutukset kevätvehnän fenologiseen
kehitykseen ja sadontuottomahdollisuuksiin
Heikki Laurila
MTT (Maa- ja elintarviketalouden tutkimuskeskus)
CERES wheat -kasvumallilla simuloitiin kohotettu-
jen CO2
:n (700 ppm, parts per million) ja lämpötilan
(+3 °C) vaikutuksia kevätvehnälajike Polkan (Triti-
cum aestivum L., cv. Polkka) fenologiseen kehityk-
seen sekä biomassa- ja sadontuottomahdollisuuksiin
optimaalisissa kasvuoloissa (potentiaalinen kasvu-
malli). Toinen simulointi suoritettiin kasvukauden
aikaisten stressitekijöiden (sää, kuivuus, sadanta ja
typpilannoitus) vaikuttaessa fenologiseen kehitykseen
ja sadontuotantoon (non-potentiaalinen kasvumalli).
Suomen ilmastonmuutos -tutkimusohjelman (SILMU
1992–1994) skenaarioiden mukaan Suomen kasvu-
olosuhteet tulevat muistuttamaan v. 2100 olosuhtei-
ta, jotka vallitsevat tällä hetkellä Tanskassa ja Poh-
jois-Saksassa. Tällöin keskilämpötila on kohonnut
3 °C ja ilmakehän CO2
-taso kaksinkertaistunut nykyi-
sestä 350:stä 700 ppm:ään.
CERES wheat -kasvumallituksen tulokset indi-
koivat kaksinkertaisen CO2
-tason kohottavan Polkka-
lajikkeen satoa 142 % potentiaalisella mallilla (167 %
non-potentiaalisella) laskettuna nykyisestä referens-
sitasosta (100 %, ambientti lämpötila, CO2
350 ppm).
Kohotettu lämpötila (+3 °C) pienensi Polkan satoa
80,4 %:iin referenssitasosta (100 %, 6,16 t ha–1
) po-
tentiaalisella mallilla (76,8 % non-potentiaalisella
mallilla referenssitasosta 4,49 t ha–1
). Kohotettu läm-
pötila lyhensi kasvin kasvuaikaa kiihdyttämällä kas-
vua vegetatiivisessa ja generatiivisessa vaiheessa.
Kasvuajan lyhentyminen puolestaan alensi Polkka-
vehnän satoa. Kun simuloitiin kohotettujen CO2
-ta-
son ja lämpötilan yhteisvaikutusta Polkan satoon,
kiihdytti kohotettu CO2
-taso vegetatiivisessa vaihees-
sa biomassan muodostumista ja generatiivisessa vai-
heessa sadonmuodostusta. Toisaalta kohotettu lämpö-
tila lyhensi kasvin generatiivista vaihetta ja pienensi
CO2
:n satoa kohottavaa vaikutusta. Tällöin kohotet-
tu lämpötila aiheutti tähkän täystuleentumisen aikai-
semmin ja sato jäi alhaisemmaksi (106 % potentiaa-
linen malli, 122 % non-potentiaalinen malli). Tulok-
set olivat samansuuntaiset Maatalouden tutkimus-
keskuksessa v. 1992–1994 Polkka kevätvehnällä teh-
tyjen open top -kasvukammio kokeiden kanssa (Ha-
kala 1998a). Simuloitaessa aikaisempaa kylvöaikaa
(15 päivää aiempi kylvö, referenssi 15.5.) sato kohosi
potentiaalisella mallilla 178 %:iin referenssitasosta
(100 %) kohotetussa lämpötilassa ja CO2
-tasossa.
